5. Tips and Tricks
This is a collection of tips and tricks that can be useful for reducing different data, or to do it slightly differently from what is presented in the example.
5.1. Bad Pixel Masks
Please note that at this time, there are no static bad pixel masks for Flamingos-2 data. DRAGONS will simply acknowledge that in the logs and continue with the reduction.
5.2. Flatfields
5.2.1. Y, J, and H-bands
Flamingos-2 Y, J and H master flats are created from lamps-on and lamps-off flats. Both types are passed in together to the “reduce” command. The order does not matter. The software separates the lamps-on and lamps-off flats and use them appropriately.
5.2.2. K-band
For K-band master flats, lamp-off flats and darks are used. In that case both flats (lamp-off only for K-band) and darks need to be fed to “reduce”. The darks’ exposure time must match that of the flats. The first input file to “reduce” must be a flat for the correct recipe library to be selected. After that the software will sort out how to use the inputs appropriately to produce the flat. For example:
$ reduce @flats_K.list @darks_for_flats.list
The K-band thermal emission from the GCAL shutter depends upon the temperature at the time of the exposure, and includes some spatial structure. Therefore the distribution of emission is not necessarily consistent, except for sequential exposures. So it is best to combine lamp-off exposures from a single day.
5.3. Checking WCS of science frames
For Flamingos-2 data, it is useful to check the World Coordinate System (WCS) of the science data. DRAGONS will fix some small discrepancy but sometimes the WCS are not written correctly in the headers causing difficulties with the sky subtraction and frame alignment.
We recommend running checkWCS on the science files.
$ reduce -r checkWCS @sci_images.list
======================================================================
RECIPE: checkWCS
======================================================================
PRIMITIVE: checkWCS
-------------------
Using S20200104S0075.fits as the reference
S20200104S0080.fits has a discrepancy of 2.00 arcsec
S20200104S0082.fits has a discrepancy of 2.01 arcsec
S20200104S0091.fits has a discrepancy of 2.01 arcsec
.
If any frames get flagged, like in the example above, you can still proceed
but after the reduction, do review the logs to check for any unusual matching
of the sources during adjustWCSToReference step, in particular the line
about the “Number of correlated sources”. If one of the highlighted frame
has a much lower number of correlated sources than the others, the algorithm
is unable to overcome the discrepancy; remove the file from the input list
and reduce again.
In general, discrepancies of the order of what is shown above do not cause problems. When the discrepancy matches the size of the dither, then you will have issues and it is best to simply remove that file from you file list right away. When such a large discrepancy happens, the WCS of that file is likely to have accidentally inherited the WCS of the previous frame which is obviously very wrong.
Note
From the API, run checkWCS like this:
checkwcs = Reduce()
checkwcs.files = list_of_science_images
checkwcs.recipename = 'checkWCS'
checkwcs.runr()
5.4. Bypassing automatic calibration association
We can think of two reasons why a user might want to bypass the calibration manager and the automatic processed calibration association. The first is to override the automatic selection, to force the use of a different processed calibration than what the system finds. The second is if there is a problem with the calibration manager and it is not working for some reason.
Whatever the specific situation, the following syntax can be used to bypass the calibration manager and set the input processed calibration yourself:
$ reduce @sci_images.list --user_cal processed_dark:S20131120S0115_dark.fits processed_flat:S20131129S0320_flat.fits
The list of recognized processed calibration is:
processed_arc
processed_bias
processed_dark
processed_flat
processed_fringe
processed_standard